Brad Boehmke

Meet Your TA

👋 [TA Name]

• [Junior/Senior/Graduate student] in Business Analytics
  
• Passionate about helping you succeed
  
• Great resource for coding questions, labs, and homework

📧 Email: [TA Email]
🕐 Office Hours: [Insert schedule or by appointment]

Data Mining is All Around Us

Organizations use data mining to drive decisions every day.

Real-World Examples:

• 🛒 Kroger analyzes loyalty card data to personalize digital coupons.
• 🎶 Spotify recommends music based on your listening history and those like you.
• 🏥 Hospitals use patient data to predict readmission risks.
• 🏈 NFL teams analyze player movement data to improve performance and strategy.
• 📦 Amazon tracks browsing behavior to recommend products and optimize inventory.

What Is Data Mining?

The process of uncovering meaningful patterns, trends, and relationships in large data sets

Why is it important?

• 📈 Helps organizations make better decisions
• 🔍 Reveals insights that would otherwise go unnoticed
• 🤖 Powers personalization, prediction, and automation
• 💰 Drives business value in nearly every industry

Activity

In-Class Challenge: Your First Day on the Job

🎉 You just landed your first internship or job at a large retail company. On Day 1, your manager says:

“We’re trying to understand what drives repeat purchases. Can you dig into this and see what you find?”

Group Activity: Where Would You Start?

Debrief: What Did You Learn?

Let’s talk through what made this challenge… challenging:

• 🧭 How did it feel to be given a vague problem?
• 📊 What did you want to know about the data before starting?
• 🧠 What tools or skills do you wish you had?
• 💬 Did your group take different approaches?

Key Takeaways:

• Real-world problems rarely come with clean instructions
• Good data work starts with asking the right questions
• This course will help you learn how to explore, analyze, and communicate insights from messy data

Why Learn Data Mining?

Data is everywhere — but insight is rare.

Regardless of your major, data mining gives you a competitive edge:

• 📊 Marketing: Understand customer behavior and optimize campaigns
• 💵 Finance: Detect fraud, model risk, and forecast performance
• 🏭 Operations: Improve efficiency, forecast demand, reduce waste
• 👩‍💼 Management: Support evidence-based decisions across teams

What You’ll Learn in BANA 4080

flowchart LR
  subgraph DM[Data Mining]
    direction LR
    subgraph Data
    end
    subgraph Cleaning
    end
    subgraph Wrangling
    end
    subgraph EDA
    end
    subgraph Modeling
    end
    subgraph Interpretation
    end
  end
  A[Stakeholders] --> DM
  B[Organizational Requirements] --> DM
  DM --> Decisions --> Value
  Data --> Cleaning --> Wrangling --> EDA --> Modeling --> Interpretation
  Interpretation --> Data

By the end of this course, you’ll be able to:

• Write basic Python code to work with data
• Clean, wrangle, and analyze messy real-world datasets
• Visualize insights clearly and effectively
• Understand how various ML/AI models are used in organizations
• Build simple ML/AI models for prediction and pattern discovery
• Communicate data-driven findings to others

What About AI? Won’t It Do This for Me?

🤖 AI tools like ChatGPT are amazing — but they don’t replace thinking with data.

Course Roadmap

Your journey through BANA 4080:

1. Weeks 1–3: Python basics & Working with data
2. Weeks 3–4: Data wrangling & Exploratory analysis
3. Weeks 5-6: Data visualization & Efficient programming
4. Week 7: Mid-term
5. Weeks 8-12: ML & AI
6. Weeks 13-14: Final project

How You’ll Learn

Each week follows a consistent rhythm:

• 🧠 Tuesday (Lecture): Learn concepts, explore examples, discuss ideas
• 💻 Thursday (Lab): Practice coding, get hands-on, work with real data

Assessments include:

• 📚 Weekly reading quizzes
  
• 📝 Biweekly homework assignments
  
• 📊 Midterm and final project
  
• ❌ No conventional tests!

Resources

Everything You Need Is in One of Two Spots

Why Python? 🤔

How You’ll Run Python: Google Colab

Next Steps